A new approach to make Autonomous Driving safe – How to drive a SOTIF process for a level 4 driverless automated driving system Subhead

As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. The paper’s objective is to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. From the results, there was less than 1 second difference between the means of gaming task (mean = 2.55s, std = 0.1002s) to no-task (mean = 2.55s, std = 0.1002s). Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments AR-Game focusing on real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.

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User Monitoring in Autonomous Driving System Using Gamified Task: A Case for VR/AR In-Car Gaming

Multimodal Technologies and Interaction ◽

10.3390/mti5080040 ◽

2021 ◽

Vol 5 (8) ◽

pp. 40

Author(s):

Joseph K. Muguro ◽

Pringgo Widyo Laksono ◽

Yuta Sasatake ◽

Kojiro Matsushita ◽

Minoru Sasaki

Keyword(s):

Secondary Task ◽

Autonomous Driving ◽

Driving Simulation ◽

Control Loop ◽

Automated Driving ◽

Driving System ◽

The Road ◽

Current Test ◽

Significant Difference ◽

Autonomous Driving System

Background: As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. This paper aims to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. Methods: We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. Results: There was a significant difference in the tasks (F(2,33) = 4.34, p = 0.0213), identifying the game-task as significant with respect to reaction time and ideal for the present investigation. Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. Conclusion: The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments with AR-Games focusing on the real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.

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Operational design domain (ODD) taxonomy for an automated driving system (ADS). Specification

10.3403/30404033 ◽

2020 ◽

Keyword(s):

Design Domain ◽

Automated Driving ◽

Driving System ◽

Operational Design

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Autonomy and Responsibility in Hybrid Systems

10.1093/oso/9780190652951.003.0003 ◽

2017 ◽

Cited By ~ 2

Author(s):

Wulf Loh ◽

Janina Loh

Keyword(s):

Hybrid Systems ◽

Hybrid System ◽

Moral Dilemma ◽

Autonomous Driving ◽

Shared Responsibility ◽

Driving System ◽

Autonomous Cars ◽

Self Driving Cars ◽

Trolley Cases ◽

Autonomous Driving System

In this chapter, we give a brief overview of the traditional notion of responsibility and introduce a concept of distributed responsibility within a responsibility network of engineers, driver, and autonomous driving system. In order to evaluate this concept, we explore the notion of man–machine hybrid systems with regard to self-driving cars and conclude that the unit comprising the car and the operator/driver consists of such a hybrid system that can assume a shared responsibility different from the responsibility of other actors in the responsibility network. Discussing certain moral dilemma situations that are structured much like trolley cases, we deduce that as long as there is something like a driver in autonomous cars as part of the hybrid system, she will have to bear the responsibility for making the morally relevant decisions that are not covered by traffic rules.

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A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2021.3086397 ◽

2021 ◽

pp. 1-18

Author(s):

Gastone Pietro Rosati Papini ◽

Alice Plebe ◽

Mauro Da Lio ◽

Riccardo Dona

Keyword(s):

Reinforcement Learning ◽

Autonomous Driving ◽

Learning Approach ◽

Driving System ◽

Autonomous Driving System

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Independent wheel control system design for highly automated driving

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211006525 ◽

2021 ◽

pp. 095440702110065

Author(s):

Shihuan Li ◽

Lei Wang

Keyword(s):

Control System ◽

Control Method ◽

Autonomous Driving ◽

Control System Design ◽

Steering Control ◽

Automated Driving ◽

Accuracy And Precision ◽

Actual Observation ◽

Control Scheme ◽

Interference Measurement

For L4 and above autonomous driving levels, the automatic control system has been redundantly designed, and a new steering control method based on brake has been proposed; a new dual-track model has been established through multiple driving tests. The axle part of the model was improved, the accuracy of the transfer function of the model was verified again through acceleration-slide tests; a controller based on interference measurement was designed on the basis of the model, and the relationships between the controller parameters was discussed. Through the linearization of the controller, the robustness of uncertain automobile parameters is discussed; the control scheme is tested and verified through group driving test, and the results prove that the accuracy and precision of the controller meet the requirements, the robustness stability is good. Moreover, the predicted value of the model fits well with the actual observation value, the proposal of this method provides a new idea for avoiding car out of control.

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Oops! It's Too Late. Your Autonomous Driving System Needs a Faster Middleware

IEEE Robotics and Automation Letters ◽

10.1109/lra.2021.3097439 ◽

2021 ◽

Vol 6 (4) ◽

pp. 7301-7308

Author(s):

Tianze Wu ◽

Baofu Wu ◽

Sa Wang ◽

Liangkai Liu ◽

Shaoshan Liu ◽

...

Keyword(s):

Autonomous Driving ◽

Driving System ◽

Autonomous Driving System

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Driver intervention performance assessment as a key aspect of L3–L4 automated vehicles deployment

Open Engineering ◽

10.1515/eng-2021-0079 ◽

2021 ◽

Vol 11 (1) ◽

pp. 845-852

Author(s):

Aleksandra Rodak ◽

Paweł Budziszewski ◽

Małgorzata Pędzierska ◽

Mikołaj Kruszewski

Keyword(s):

Performance Assessment ◽

Minimal Risk ◽

Automated Vehicles ◽

Automated Driving ◽

Driving System ◽

Driving Task ◽

Driving Conditions

Abstract In L3–L4 vehicles, driving task is performed primarily by automated driving system (ADS). Automation mode permits to engage in non-driving-related tasks; however, it necessitates continuous vigilance and attention. Although the driver may be distracted, a request to intervene may suddenly occur, requiring immediate and appropriate response to driving conditions. To increase safety, automated vehicles should be equipped with a Driver Intervention Performance Assessment module (DIPA), ensuring that the driver is able to take the control of the vehicle and maintain it safely. Otherwise, ADS should regain control from the driver and perform a minimal risk manoeuvre. The paper explains the essence of DIPA, indicates possible measures, and describes a concept of DIPA framework being developed in the project.

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